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This manuscript presents a working model linking chemical disorder and transport properties in correlated-electron perovskites with high-entropy formulations and a framework to actively design them. We demonstrate this new learning in epitaxial Sr$x$(Ti,Cr,Nb,Mo,W)O$3$ thin films that exhibit exceptional crystalline fidelity despite a diverse chemical formulation where most B-site species are highly misfit with respect to valence and radius. X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy confirm a unique combination of chemical disorder and structural perfection in thick epitaxial layers. This combination produces significant electron correlation, low electrical resistivity, and an optical transparency window that surpasses that of constituent end-members, with a flattened frequency- and temperature-dependent response. We address the computational challenges of modeling such systems and investigate short-range ordering using cluster expansion. These results showcase that unusual d-metal combinations access an expanded property design space that is predictable using end-member characteristics -- though unavailable to them -- thus offering performance advances in optical, spintronic, and quantum devices.

Throttling in graphs optimizes a sum or product of resources used, such as the number of vertices in an initial set, and time required, such as the propagation time, to complete a given task. We introduce a new technique to establish sharp upper bounds in terms of graph order for sum throttling and initial cost product throttling for power domination. Furthermore, we establish sharp bounds on possible changes of the product throttling number, both with and without initial cost, caused by certain graph operations for standard zero forcing, positive semidefinite forcing, and power domination.

International enterprises, organizations, or hospitals collect large amounts of multi-modal data stored in databases, text documents, images, and videos. While there has been recent progress in the separate fields of multi-modal data exploration as well as in database systems that automatically translate natural language questions to database query languages, the research challenge of querying database systems combined with other unstructured modalities such as images in natural language is widely unexplored. In this paper, we propose XMODE - a system that enables explainable, multi-modal data exploration in natural language. Our approach is based on the following research contributions: (1) Our system is inspired by a real-world use case that enables users to explore multi-modal information systems. (2) XMODE leverages a LLM-based agentic AI framework to decompose a natural language question into subtasks such as text-to-SQL generation and image analysis. (3) Experimental results on multi-modal datasets over relational data and images demonstrate that our system outperforms state-of-the-art multi-modal exploration systems, excelling not only in accuracy but also in various performance metrics such as query latency, API costs, planning efficiency, and explanation quality, thanks to the more effective utilization of the reasoning capabilities of LLMs.

The parameter $q(G)$ of an $n$-vertex graph $G$ is the minimum number of distinct eigenvalues over the family of symmetric matrices described by $G$. We show that all $G$ with $e(\overline{G}) = |E(\overline{G})| \leq \lfloor n/2 \rfloor -1$ have $q(G)=2$. We conjecture that any $G$ with $e(\overline{G}) \leq n-3$ satisfies $q(G) = 2$. We show that this conjecture is true if $\overline{G}$ is bipartite and in other sporadic cases. Furthermore, we characterize $G$ with $\overline{G}$ bipartite and $e(\overline{G}) = n-2$ for which $q(G) > 2$.

We present a series of five new broadband X-ray observations of the ultraluminous X-ray source Holmberg IX X-1, performed by $XMM$-$Newton$ and $NuSTAR$ in coordination. The first three of these show high soft X-ray fluxes but a near total collapse of the high-energy ($\gtrsim$15 keV) emission, previously seen to be surprisingly stable across all prior broadband observations of the source. The latter two show a recovery in hard X-rays, remarkably once again respecting the same stable high-energy flux exhibited by all of the archival observations. We also present a joint analysis of all broadband observations of Holmberg IX X-1 to date (encompassing 11 epochs in total) in order to investigate whether it shows the same luminosity-temperature behaviour as NGC 1313 X-1 (which also shows a stable high-energy flux), whereby the hotter disc component in the spectrum exhibits two distinct, positively-correlated tracks in the luminosity-temperature plane. Holmberg IX X-1 may show similar behaviour, but the results depend on whether the highest energy emission is assumed to be an up-scattering corona or an accretion column. The strongest evidence for this behaviour is found in the former case, while in the latter the new 'soft' epochs appear distinct from the other high-flux epochs. We discuss possible explanations for these new 'soft' spectra in the context of the expected structure of super-Eddington accretion flows around black holes and neutron stars, and highlight a potentially interesting analogy with the recent destruction and re-creation of the corona seen in the AGN 1ES 1927+654., Comment: 15 pages, 5 figures, submitted for publication in MNRAS (comments welcome)

This paper reports on a scientometric analysis bolstered by human in the loop, domain experts, to examine the field of metal organic frameworks (MOFs) research. Scientometric analyses reveal the intellectual landscape of a field. The study engaged MOF scientists in the design and review of our research workflow. MOF materials are an essential component in next generation renewable energy storage and biomedical technologies. The research approach demonstrates how engaging experts, via human in the loop processes, can help develop a comprehensive view of a field research trends, influential works, and specialized topics.

High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rock salt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rock salt compositions are correctly resolved, with dozens more compositions awaiting discovery.

This paper introduces the Pandemic PACT Advanced Categorisation Engine (PPACE) along with its associated dataset. PPACE is a fine-tuned model developed to automatically classify research abstracts from funded biomedical projects according to WHO-aligned research priorities. This task is crucial for monitoring research trends and identifying gaps in global health preparedness and response. Our approach builds on human-annotated projects, which are allocated one or more categories from a predefined list. A large language model is then used to generate `rationales' explaining the reasoning behind these annotations. This augmented data, comprising expert annotations and rationales, is subsequently used to fine-tune a smaller, more efficient model. Developed as part of the Pandemic PACT project, which aims to track and analyse research funding and clinical evidence for a wide range of diseases with outbreak potential, PPACE supports informed decision-making by research funders, policymakers, and independent researchers. We introduce and release both the trained model and the instruction-based dataset used for its training. Our evaluation shows that PPACE significantly outperforms its baselines. The release of PPACE and its associated dataset offers valuable resources for researchers in multilabel biomedical document classification and supports advancements in aligning biomedical research with key global health priorities.

We consider bounds on maximum nullity of a graph via transversal numbers of compatible collections of forts. Results include generalizations of theorems from symmetric to combinatorially symmetric matrices, special bases of matrix nullspaces derived from transversal sets, and examples of issues that arise when considering only minimal forts and how to avoid them. We also show an important difference between constructing symmetric and combinatorially symmetric matrices associated to a graph whose nullspaces are supported on collections of disjoint forts.

We unravel the distinct roles each cation plays in phase evolution, stability, and properties within Mg1/5Co1/5Ni1/5Cu1/5Zn1/5O high-entropy oxide (HEO) by integrating experimental findings, thermodynamic analyses, and first-principles predictions. Our approach is through sequentially removing one cation at a time from the five-component high-entropy oxide to create five four-component derivatives. Bulk synthesis experiments indicate that Mg, Ni, and Co act as rock salt phase stabilizers whereas only Mg and Ni enthalpically enhance single-phase rock salt stability in thin film growth; synthesis conditions dictate whether Co is a rock salt phase stabilizer or destabilizer. By examining the competing phases and oxidation state preferences using pseudo-binary phase diagrams and first-principles calculations, we resolve the stability differences between bulk and thin film for all compositions. We systematically explore HEO macroscopic property sensitivity to cation selection employing both predicted and measured optical spectra. This study establishes a framework for understanding high-entropy oxide synthesizability and properties on a per-cation basis that is broadly applicable to tailoring functional property design in other high-entropy materials., Comment: Saeed S. I. Almishal and Jacob T. Sivak contributed equally to this work

This study introduces a biologically-inspired model designed to examine the role of coincidence detection cells in speech segregation tasks. The model consists of three stages: a time-domain cochlear model that generates instantaneous rates of auditory nerve fibers, coincidence detection cells that amplify neural activity synchronously with speech presence, and an optimal spectro-temporal speech presence estimator. A comparative analysis between speech estimation based on the firing rates of auditory nerve fibers and those of coincidence detection cells indicates that the neural representation of coincidence cells significantly reduces noise components, resulting in a more distinguishable representation of speech in noise. The proposed framework demonstrates the potential of brainstem nuclei processing in enhancing auditory skills. Moreover, this approach can be further tested in other sensory systems in general and within the auditory system in particular.

The colliding-wind region in binary systems made of massive stars allows us to investigate various aspects of shock physics, including particle acceleration. Particle accelerators of this kind are tagged as Particle-Accelerating Colliding-Wind Binaries, and are mainly identified thanks to their synchrotron radio emission. Our objective is first to validate the idea that obtaining snapshot high-resolution radio images of massive binaries constitutes a relevant approach to unambiguously identify particle accelerators. Second, we intend to exploit these images to characterize the synchrotron emission of two specific targets, HD167971 and HD168112, known as particle accelerators. We traced the radio emission from the two targets at 1.6 GHz with the European Very Long Baseline Interferometry Network, with an angular resolution of a few milli-arcseconds. Our measurements allowed us to obtain images for both targets. For HD167971, our observation occurs close to apastron, at an orbital phase where the synchrotron emission is minimum. For HD168112, we resolved for the very first time the synchrotron emission region. The emission region appears slightly elongated, in agreement with expectation for a colliding-wind region. In both cases the measured emission is significantly stronger than the expected thermal emission from the stellar winds, lending strong support for a non-thermal nature. Our study brings a significant contribution to the still poorly addressed question of high angular resolution radio imaging of colliding-wind binaries. We show that snapshot Very Long Baseline Interferometry measurements constitute an efficient approach to investigate these objects, with promising results in terms of identification of additional particle accelerators, on top of being promising as well to reveal long period binaries., Comment: 8 pages, 1 figure, accepted for publication in A&A

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Frameworks (MOFs). The MOF case study involved natural language processing to extract key synthesis experiment information from a corpus of research literature. Following, a classification activity was performed by domain experts to identify entity-relation pairs. Results include: 1) a research framework for metadata design, 2) a metadata schema that includes nine entities and two relationships for reporting MOF synthesis experiments, and 3) a growing database of MOF synthesis reports structured by our metadata scheme. The metadata schema is intended to support discovery and reproducibility of metal-organic framework research and the FAIR principles. The paper provides background information, identifies the research goals and objectives, research design, results, a discussion, and the conclusion., Comment: Accepted by the 17th International Conference on Metadata and Semantics Research

We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing the LLM, ChatGPT, to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials., Comment: In 17th International Conference on Metadata and Semantics Research, October 2023

We investigate the sparsity of null vectors of real symmetric matrices whose off-diagonal pattern of zero and nonzero entries is described by the adjacencies of a graph. We use the definition of the spark of a matrix, the smallest number of nonzero coordinates of any null vector, to define the spark of a graph as the smallest possible spark of a corresponding matrix. We study connections of graph spark to well-known concepts including minimum rank, forts, orthogonal representations, Parter and Fiedler vertices, and vertex connectivity., Comment: 20 pages, 2 figures

For an $n \times n$ matrix $A$, let $q(A)$ be the number of distinct eigenvalues of $A$. If $G$ is a connected graph on $n$ vertices, let $\mathcal{S}(G)$ be the set of all real symmetric $n \times n$ matrices $A=[a_{ij}]$ such that for $i\neq j$, $a_{ij}=0$ if and only if $\{i,j\}$ is not an edge of $G$. Let $q(G)={\rm min}\{q(A)\,:\,A \in \mathcal{S}(G)\}$. Studying $q(G)$ has become a fundamental sub-problem of the inverse eigenvalue problem for graphs, and characterizing the case for which $q(G)=2$ has been especially difficult. This paper considers the problem of determining the regular graphs $G$ that satisfy $q(G)=2$. The resolution is straightforward if the degree of regularity is $1, 2,$ or $3$. However, the $4$-regular graphs with $q(G)=2$ are much more difficult to characterize. A connected $4$-regular graph has $q(G)=2$ if and only if either $G$ belongs to a specific infinite class of graphs, or else $G$ is one of fifteen $4$-regular graphs whose number of vertices ranges from $5$ to $16$. This technical result gives rise to several intriguing questions., Comment: AMS subject classification: 05C50, 15A29, 15A18

Winds of massive stars are suspected to be inhomogeneous (or clumpy), which biases the measures of their mass loss rates. In High Mass X-ray Binaries (HMXBs), the compact object can be used as an orbiting X-ray point source to probe the wind and constrain its clumpiness. We perform spectro-timing analysis of the HMXB OAO 1657-415 with non-simultaneous NuSTAR and NICER observations. We compute the hardness ratio from the energy-resolved light curves, and using an adaptive rebinning technique, we thus select appropriate time segments to search for rapid spectral variations on timescales of a few hundreds to thousands of seconds. Column density and intensity of Iron K$\alpha$ line were strongly correlated, and the recorded spectral variations were consistent with accretion from a clumpy wind. We also illustrate a novel framework to measure clump sizes, masses in HMXBs more accurately based on absorption measurements and orbital parameters of the source. We then discuss the limitations posed by current X-ray spacecrafts in such measurements and present prospects with future X-ray missions. We find that the source pulse profiles show a moderate dependence on energy. We identify a previously undetected dip in the pulse profile visible throughout the NuSTAR observation near spin phase 0.15 possibly caused by intrinsic changes in accretion geometry close to the neutron star. We do not find any evidence for the debated cyclotron line at $\sim$ 36\,keV in the time-averaged or the phase-resolved spectra with NuSTAR., Comment: 32 pages, 11 figures in main text, 7 figures in Appendix, Accepted for publication in ApJ

The Gaia-ESO survey sample of massive OB stars in the Carina Nebula consists of 234 stars. The addition of brighter sources from the Galactic O-Star Spectroscopic Survey and additional sources from the literature allows us to create the most complete census of massive OB stars done so far in the region. It contains a total of 316 stars, being 18 of them in the background and four in the foreground. Of the 294 stellar systems in Car OB1, 74 are of O type, 214 are of non-supergiant B type and 6 are of WR or non-O supergiant (II to Ia) spectral class. We identify 20 spectroscopic binary systems with an O-star primary, of which 6 are reported for the first time, and another 18 with a B-star primary, of which 13 are new detections. The average observed double-lined binary fraction of O-type stars in the surveyed region is 0.35, which represents a lower limit. We find a good correlation between the spectroscopic n-qualifier and the projected rotational velocity of the stars. The fraction of candidate runaways among the stars with and without the n-qualifier is 4.4% and 2.4%, respectively, although non resolved double-lined binaries can be contaminating the fast rotators sample., Comment: Accepted for publication in A&A, 62 pages, 8 figures

Convolutional neural networks (CNNs) are one of the most popular models of Artificial Neural Networks (ANN)s in Computer Vision (CV). A variety of CNN-based structures were developed by researchers to solve problems like image classification, object detection, and image similarity measurement. Although CNNs have shown their value in most cases, they still have a downside: they easily overfit when there are not enough samples in the dataset. Most medical image datasets are examples of such a dataset. Additionally, many datasets also contain both designed features and images, but CNNs can only deal with images directly. This represents a missed opportunity to leverage additional information. For this reason, we propose a new structure of CNN-based model: CompNet, a composite convolutional neural network. This is a specially designed neural network that accepts combinations of images and designed features as input in order to leverage all available information. The novelty of this structure is that it uses learned features from images to weight designed features in order to gain all information from both images and designed features. With the use of this structure on classification tasks, the results indicate that our approach has the capability to significantly reduce overfitting. Furthermore, we also found several similar approaches proposed by other researchers that can combine images and designed features. To make comparison, we first applied those similar approaches on LIDC and compared the results with the CompNet results, then we applied our CompNet on the datasets that those similar approaches originally used in their works and compared the results with the results they proposed in their papers. All these comparison results showed that our model outperformed those similar approaches on classification tasks either on LIDC dataset or on their proposed datasets.

Hercules X-1 is a nearly edge-on accreting X-ray pulsar with a warped accretion disk, precessing with a period of about 35 days. The disk precession allows for unique and changing sightlines towards the X-ray source. To investigate the accretion flow at a variety of sightlines, we obtained a large observational campaign on Her X-1 with XMM-Newton (380 ks exposure) and Chandra (50 ks exposure) for a significant fraction of a single disk precession cycle, resulting in one of the best datasets taken to date on a neutron star X-ray binary. Here we present the spectral analysis of the High State high-resolution grating and CCD datasets, including the extensive archival data available for this famous system. The observations reveal a complex Fe K region structure, with three emission line components of different velocity widths. Similarly, the high-resolution soft X-ray spectra reveal a number of emission lines of various widths. We correct for the uncertain gain of the EPIC-pn Timing mode spectra, and track the evolution of these spectral components with Her X-1 precession phase and observed luminosity. We find evidence for three groups of emission lines: one originates in the outer accretion disk (10^5 RG from the neutron star). The second line group plausibly originates at the boundary between the inner disk and the pulsar magnetosphere (10^3 RG). The last group is too broad to arise in the magnetically-truncated disk and instead must originate very close to the neutron star surface, likely from X-ray reflection from the accretion curtain (~10^2 RG)., Comment: Accepted for publication in ApJ. 26 pages, 17 figures, 4 tables

Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, catalysis, and drug delivery. The Cambridge Structural Database (CSD) reports 10,636 synthesized MOF crystals which in addition contains ca. 114,373 MOF-like structures. The sheer number of synthesized (plus potentially synthesizable) MOF structures requires researchers pursue computational techniques to screen and isolate MOF candidates. In this demo paper, we describe our effort on leveraging knowledge graph methods to facilitate MOF prediction, discovery, and synthesis. We present challenges and case studies about (1) construction of a MOF knowledge graph (MOF-KG) from structured and unstructured sources and (2) leveraging the MOF-KG for discovery of new or missing knowledge., Comment: Accepted by the International Workshop on Knowledge Graphs and Open Knowledge Network (OKN'22) Co-located with the 28th ACM SIGKDD Conference

The parameter $q(G)$ of a graph $G$ is the minimum number of distinct eigenvalues over the family of symmetric matrices described by $G$. It is shown that the minimum number of edges necessary for a connected graph $G$ to have $q(G)=2$ is $2n-4$ if $n$ is even, and $2n-3$ if $n$ is odd. In addition, a characterization of graphs for which equality is achieved in either case is given., Comment: 13 pages